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Navigation control of microrobots in vivo has great potential in precision medicine and has attracted considerable attention in recent years. The control performance of the existing methods is considerably affected by hysteresis nonlinearity. This article presents a robust control method that can overcome hysteresis influence in navigating a microrobot actuated by an electromagnetic coil system. A motion planner that combines the breadth-first search (BFS) method and genetic algorithm (GA) is used to plan a reliable and flexible trajectory for the microrobot navigation. To compensate for hysteresis nonlinearity existing in the system, the Prandtl-Ishlinskii (PI) model is introduced. A robust controller that integrates adaptive sliding mode control (ASMC) and nonlinear disturbance observer is designed to guarantee the stability and accuracy of the microrobot in motion. Experiments have been performed to demonstrate the effectiveness of the proposed approach. The success of this research will advance the microrobot navigation for in vivo applications.

Automatic 3D navigation control of microrobot in large microenvironment is one of the primary challenges hindering its applications. In this article, we present a systematic approach to use an electromagnetic manipulation system to deal with this challenge. Two movable orthogonal microscopic cameras scan the microenvironment on the top view and side view to build a stereo occupancy map automatically. The initial position of microrobot can be located on the basis of the similarity curves generated by the scanning sequences. In accordance with the distribution characteristic of electromagnetic fields in workspace, an enhanced rapidly exploring random tree algorithm is proposed to avoid obstacles in the complex environment. To ensure continuous visual servo in wide range, a field-of-view tracking method is developed using a specific image definition evaluation algorithm. A prescribed performance controller with disturbance observer is designed, which guarantees that the microrobot can remain in the view of microscopic cameras, and the transient and steady-state performance of the system can satisfy the expected requirements. A set of experiments are performed to verify the effectiveness of the proposed automatic 3D navigation strategy. Experimental results show that the microrobot can navigate automatically in a $\bm {12\,\times 10\,\times \text{10-mm}}$ microenvironment containing obstacles, and achieve reliable field-of-view tracking and path following at high speed.

This study investigates the use of a tendon-driven continuum robot to deform a soft object, whereas the robot body is deformed into an arbitrary shape to adapt to a constrained environment. A dynamic estimator (DE) is developed to approximate the Jacobian matrix that associates the actuator input with the deformed output of the soft object. This helps solve the singularity problem and reduce the effects of noise. Then a visual predictive controller (VPC) with a reference trajectory is developed to ensure a smooth operation. A linear extended-state observer (ESO) is further designed to measure the robot states, such that the controller can compensate for the estimation error. Simulations and experiments are performed to verify the proposed control approach. Note to Practitioners —The motivation of this article is to solve the problem of automatic deformation control of soft objects in restricted environments. The existing soft object deformation control is achieved using rigid robots in an open environment, but rigid robots are difficult to use in specific applications where the environment is restricted (e.g., natural orifice surgery). Flexible continuum robots with mechanical compliance can manipulate soft objects in narrow spaces. However, due to environmental constraints, the robot body may be deformed into any shape regardless of the input of the actuator. To solve the problem, this research provides a new visual servo control strategy that deforms soft objects using a continuum robot in a restricted environment. The proposed method can control a flexible robot to manipulate soft objects while taking into account the change in the robot configuration in a restricted environment.

The use of microrobots to achieve micromanipulation in vivo has attracted considerable attention in recent years to meet the request of non-invasiveness, precision and high efficiency in medical treatment. This letter reports the use of a home-designed electromagnetic manipulation system to control the movements of microrobots in a simulated vascular structure. After dynamic modeling, the moving trajectory of the microrobot is designed on the basis of an artificial potential field. Estimator for position is then designed with stability analysis by a Lyapunov approach. A super-twisting algorithm is further applied to control the microrobot to move along with the desired trajectory. Simulations and experiments are finally performed to demonstrate the effectiveness of the proposed control approach.

Based on the backstepping control theory, this paper investigates the distributed finite-time coordinated tracking control for Euler–Lagrange systems under directed graphs. We consider that only a portion of followers can receive information from the dynamic leader. Two cases are discussed in this paper: (1) the system having parameter uncertainties which can be linearized, and (2) the system having structured uncertainties and external disturbances which cannot be linearized. For the first case, parameter-linearity property is used to approximate the parametric uncertainties. For the second case, neural networks are used to approximate the nonlinear uncertainties and external disturbances. For the controller design, first, we design an auxiliary variable. Then, backstepping method and Lyapunov stability theory are used to prove that the tracking errors and adaptive estimation errors are bounded. Finally, the finite-time convergence property of the tracking errors is proved by increasing control gains. Numerical examples and comparisons with other methods are provided to show the effectiveness and superiorities of the proposed methods.

In this paper, under-directed topology, distributed coordinated tracking control schemes are proposed for multiple Euler–Lagrange systems with time-varying communication delays, nonlinear uncertainties, and external disturbances. Concerning with different leader velocities, both constant leader velocity case and time-varying leader velocity case are addressed by designing two different distributed observers. Combining with the proposed distributed leader velocity observers, two coordinated tracking control schemes are developed by the effort of neural network approximation and sliding mode technique, which can compensate the nonlinearities and uncertainties. For the first case, tracking errors are rigorously proved to be globally asymptotically converged by using Lyapunov–Krasovskii method. To further eliminate chattering caused by the discontinuous sign function, the saturation function is used for the second case, and the proposed control algorithm ensures the same convergence of tracking errors via Lyapunov analysis. Finally, the effectiveness of the proposed distributed tracking control schemes is verified by the numerical examples.

Objective: The application of in vivo microrobot navigation has received considerable attention from the field of precision therapy, which uses microrobots in living organisms. Methods: This study investigates the navigation of microrobots in vivo using optical coherence tomography (OCT) imaging feedback. The electromagnetic gradient field generated by a home-made electromagnetic manipulation system is magnetically modeled. With this model, the magnetic force acting on the microrobot is calculated, and the relationship between this force and the velocity of the microrobot is characterized. Results: Results are verified through in vitro experiments wherein microrobots are driven in three types of fluid, namely, normal saline, gastric juice, and mouse urine. In vivo experiments are performed to navigate the microrobot in a mouse portal vein in which the OCT imaging system tracks the microrobot in vivo . Conclusions: Experimental results demonstrate the effectiveness of the proposed approach. The microrobots can be magnetically driven in the in vivo environment using the OCT imaging feedback. Significance: The significance of this study lies in providing a new method of driving microrobots in vivo .

This study mainly investigates the problem of distributed tracking control for time-varying delay existing multiple Euler-Lagrange systems considering full-state constraints and input saturation under the directed graph. Specifically, the system under consideration consists of system uncertainties and external disturbances. In the control law design, a distributed observer is first designed that the followers can obtain the leader's time-varying information. Then the barrier Lyapunov function technique is used to make sure the system errors can converge to a certain range while the anti-windup method is utilized to overcome the influence of control input saturation. Further, in order to prevent chattering, an adaptive law is given. Numerical simulations are given to verify the proposed algorithms.

In this research, we mainly proposed a distributed containment control algorithm for Euler-Lagrange system, where general disturbances are considered. Also directed topologies are used to describe the leader-following case. First, we introduced the error variables, linear sliding variable, and terminal sliding variable. Then the control algorithm was proposed for the EL system expressed by EL equations. By using the Lyapunov theory and some lemmas, we proved the control errors of the system converge to the original point along the sliding mode. The followers can converge to the convex hull formed by the leaders in finite time.

Based on the backstepping method, this paper investigates the distributed finite-time tracking control problem for Euler-Lagrange systems under directed graph. We consider that only some of the followers can receive information of the dynamic leader. There exist system parametric uncertainties which can be linearized while parameter-linearity property is used to approximate the uncertainties. Backstepping method and Lyapunov stability theory are utilized to prove that the tracking errors and adaptive estimation errors are bounded. Further, the finite-time convergence property of the tracking errors is proved by increasing control gains. Numerical simulations are provided to show the effectiveness of the proposed control algorithm.

This paper designed a finite-time configuration control law that can weaken chattering under the directed communication topology. The leader information is available to only a portion of the followers and the existence of generalized disturbance in the dynamics model is considered. By defining the error function of relative configuration and selecting the appropriate variables of the finite-time high-order sliding-mode control, the sign function appears in the derivative of the control force, a strictly distributed configuration control law with the integral form of sign function is obtained and improves the convergence speed while reducing the chattering. An adaptive estimation law is proposed when the upper bound of the generalized disturbance is unknown. The finite-time characteristics of the proposed distributed adaptive control law are proved by Lyapunov method. Through the simulation analysis and comparison with other algorithm, the validity of the proposed algorithm is verified.